User modelling and metadata of transmedia content data

ABSTRACT

There is provided a system for linking transmedia content subsets. A memory stores a plurality of transmedia content data items and associated linking data which define time-ordered content links between the plurality of transmedia content data items. The plurality of transmedia content data items are arranged into linked transmedia content subsets comprising different groups of the transmedia content data items and different content links therebetween; a transmedia content model that represents the transmedia content data items as nodes and the content links between the transmedia content data items as edges in one or more time-varying graphs. A processor is configured to associate the transmedia content data items with the time-ordered content links and store the linking data in the memory. It assigns the transmedia content data items to nodes of a graph structure, assign the time-ordered content links to edges of the graph structure and store them in the transmedia content model. There is also provided a system for generating metadata for non-linearly connected transmedia content.

FIELD OF DISCLOSURE

The present disclosure relates to apparatus, systems and methods forprocessing transmedia content. For example, the disclosure provides formodelling user interaction with transmedia content data and generatingdata therefor.

BACKGROUND

Influenced by a variety of different multimedia content types, newdigital distribution channels, mobile communication devices and an everincreasing use of social media, industry is currently experiencing adisruption in how media is created, distributed and consumed. Classicalproduction pipelines have become less effective as audiences movetowards anytime, anywhere, personalized consumption, substitutingTV-centric models with multi-device, multichannel models. Individualcustomers and groups of customers have also become more interactive andparticipatory, contributing significantly to the creation of new media.The cycles in the traditional creation-distribution-consumption loopbecome much shorter as consumers constantly provide feedback, resultingin a trend towards ultrashort form content.

Existing delivery platforms, for example YouTube and Facebook, allow forthe creation and editing of simple channel based content, using a basicmodel whereby content creators can upload content such as video, text orimages, and users can consume the content in an isolated, linear andmono-medial manner. This can often be done in conjunction with mediapresented via other platforms such as television or print media.

At the same time, functionality provided by existing multimediaplatforms allows the sharing of user-generated content, which, alongwith social networking, is transforming the media ecosystem. Mobilephones, digital cameras and other pervasive devices produce huge amountsof data that users can continuously distribute in real time.Consequently, content sharing and distribution needs will continue toincrease. The content can be of many different forms, known collectivelyas “transmedia” content.

Existing systems that allow users to generate, organize and sharecontent are generally hard to control: these systems do not offeradequate tools for predicting what the next big trend will be, and whichgroupings of time-ordered content resonate with particular audiences.Furthermore, visualising the large amount of multimedia information in away which users can explore and consume is challenging. In particular,visualisation of such large data sets is challenging in terms ofperformance, especially on lower-power devices such as smartphones ortablets. It is desirable that any visualisation of the data could berendered in real time such that immediate visual feedback is provided toa user exploring the data. This can be particularly problematic whencontent from different users and of different types needs to be groupedin a time-ordered manner such that individual items of content may beshared and linked to each other across multiple groups in a non-linearmanner.

Finally, the ever-growing availability of content to multiple users andthe ever-increasing power of computing resources available to individualusers is driving users towards their own individual creation of content,with such content being in multiple formats. This progression can beseen in FIG. 1. User 10 consumes single content items 15. Withincreasing computing resources, user 20 has developed into aninteractive user making choices which affect the flow of individualcontent items 25 to the user 20. Further, user 30 has recently becomemore common by generating multiple personalised, individual contentitems 35 which can be accessed over the Internet 50 by other users. Aproblem now exists with a current user 40 who can access a considerableamount of different types of content items 35 over the Internet 50 anddesires to utilise such content. It would be desirable for user 40 to beable to contribute to and generate new structured groups 46 of linkedcontent items 45. Further, it is desirable to be able to generate andstore data pertaining to the structured groups 46 of linked contentitems 45 in manner which makes processing of the data more efficient.

SUMMARY OF THE DISCLOSURE

In a first aspect of this disclosure, there is provided an apparatus forlinking transmedia content subsets comprising:

a memory configured to store:

-   -   a plurality of transmedia content data items and associated        linking data which define time-ordered content links between the        plurality of transmedia content data items, the plurality of        transmedia content data items being arranged into linked        transmedia content subsets comprising different groups of the        transmedia content data items and different content links        therebetween;    -   a transmedia content model that represents the transmedia        content data items as nodes and the content links between the        transmedia content data items as edges in one or more        time-varying graphs;

processing circuitry configured to:

-   -   associate the transmedia content data items with the        time-ordered content links and store the linking data in the        memory; and    -   assign the transmedia content data items to nodes of a graph        structure, assign the time-ordered content links to edges of the        graph structure;    -   store data pertaining to the nodes and edges in the transmedia        content model.

The apparatus thus provides for transmedia content items to be linked itto other items of transmedia content in a time-ordered fashion, andstored in an accessible structure which can be accessed, and thusutilised readily by user devices.

The apparatus may be implemented as a computing device, for example aserver computing device, which may be a back end server device.

The processing circuitry may be further configured to implement aharvesting engine configured to identify a number of common nodes and/ora number of common edges shared between at least two of the linkedtransmedia content subsets; and extract from the memory one or morelinked transmedia content subsets that share common edges and/or nodeswhen the number of common edges and/or nodes exceeds a pre-definedthreshold.

The harvesting engine may be configured to extract the one or morelinked transmedia content subsets by copying the transmedia content dataitems and associated linking data of the extracted transmedia contentsubset to another region in the memory.

The harvesting engine may be configured to extract the one or morelinked transmedia content subsets by storing a reference to eachtransmedia content data items and each item of associated linking dataof the extracted transmedia content subset in the memory.

The transmedia content data items can relate to narrative elements ofthe transmedia content data items. The time-ordered content links candefine a narrative order of the transmedia content data items.

Each time-ordered content link can define a directional link from afirst transmedia content data item to a second transmedia content dataitem of the plurality of transmedia content data items. Each firsttransmedia content data item can have a plurality of outgoingtime-ordered content links. Each second transmedia content data item canhave a plurality of incoming time-ordered content links.

The memory may be further configured to store a plurality of subsetentry points for the plurality of transmedia content subsets.

Each subset entry point may be a flag indicating a transmedia contentdata item that has at least one outgoing time-ordered link and noincoming time-ordered links.

Each linked transmedia content subset can define a linear path, whereina linear path comprises a subset entry point, one or more transmediacontent data items and one or more time ordered links between the subsetentry point and the transmedia content data items. Two or moretransmedia content subsets can share one or more subset entry points,one or more transmedia content data items and/or one or more timeordered content links.

The two or more transmedia content subsets may form a non-linear networkof transmedia content data items connected by time ordered contentlinks, and the non-linear network may include a plurality of subsetentry points and/or a plurality of subset end points.

The processing circuitry may be further configured to calculate anentropy value for a transmedia content subset based on the number oftransmedia content data items and/or time-ordered content links sharedwith other transmedia content subsets in the same non-linear network.

The processing circuitry may be further configured to calculate atemperature value for a transmedia content subset based on a number ofsocial media endorsements received by the transmedia content subset ortransmedia content data items present in the transmedia content subset.

The processing circuitry may be further configured to calculate a volumevalue for the transmedia content subset based on the number oftransmedia content data items present in the transmedia content subset.

The processing circuitry may be further configured to calculate apressure value based on the calculated temperature and volume values.

The processing circuitry may be further configured to extract the one ormore linked transmedia content subsets for which one or more of theentropy, pressure, volume or time exceeds a predetermined threshold.

The memory may further comprise a plurality of transmedia contentmetadata items, each transmedia content metadata item being associatedwith one or more transmedia content data items, and a plurality ofsubset metadata items, each subset metadata item being associated withone or more transmedia content subsets.

Each transmedia content metadata item may defines narrative informationfor each transmedia content data item.

The step of extracting may be carried out only for transmedia contentsubsets in which the transmedia content metadata items indicate acomplete narrative.

In a second aspect of this disclosure, there is provided an apparatusfor generating metadata for non-linearly connected transmedia contentcomprising:

-   -   a memory configured to store:        -   a plurality of transmedia content data items and associated            linking data which define time-ordered content links between            the plurality of transmedia content data items, whereby the            plurality of transmedia content data items are arranged into            linked transmedia content subsets comprising different            groups of the transmedia content data items and different            content links therebetween; and        -   one or more metadata items associated with one or more of            the plurality of transmedia content data items;    -   a metadata generation engine configured to generate metadata        associated with a given subset of transmedia content data items        based on one or more of the metadata items associated with the        transmedia content data items comprised within the given subset.

The apparatus thus provides for transmedia content items to be linked toother items of transmedia content in subsets in a time-ordered fashion,and metadata data to be automatically generated for the entire subset ofdata. Thus, the subsets of data are readily accessible to users fromuser devices, and can be manipulated and utilised with ease.

The metadata generation engine may be configured to generate metadataassociated with the subset of transmedia content data items by groupingthe metadata associated with the transmedia content data items of thetransmedia content subset and copying the grouped metadata to anotherregion in the memory.

The metadata generation engine may be configured to generate metadataassociated with the subset of transmedia content data items by groupingthe metadata associated with the transmedia content data items of thetransmedia content subset and copying references to the grouped metadatato another region in the memory.

The metadata generation engine may be further configured to generate theone or more metadata items associated with the plurality of transmediacontent data items stored in the memory.

The metadata generation engine may be configured to generateautomatically metadata by analysing media elements, for example elementsof the type being audio, video, text, images, games, or graphstructures. This generation may take place in real time whilst a user isaccessing and utilising the content items from the apparatus via a userdevice

The metadata generation engine may be configured to extract and analyzefeatures from the transmedia content data items.

The metadata generation engine may be configured to generate metadatafrom input received at a user input interface in communication with theapparatus.

The metadata generation engine may further comprise a metadataaggregator, wherein the metadata aggregator is configured receivemetadata from one or more sources, analyse the received metadata andassociate the analysed metadata with the transmedia content data itemsand transmedia content subsets.

The metadata aggregator may analyse the metadata received from theplurality of sources to determine metadata tags to associate with thetransmedia content data items and transmedia content subsets.

In a third aspect of this disclosure, there is provided a systemcomprising:

-   -   the aforementioned apparatus; and    -   an electronic device configured to be in communication with the        apparatus and display one or more of the transmedia content        items received from the apparatus.

The electronic device may be implemented as user equipment, which may bea computing device, for example a client computing device, such aspersonal computer, laptop or a handheld computing device. For example,the handheld computing device may be a mobile phone or smartphone. Theelectronic device may include a user input interface configured toprovide user input to the processing circuitry, such as for example theinstructions to create a new time-ordered content link. This may be oneor more of a keypad or touch-sensitive display. The electronic devicemay include a display (which may include the touch-sensitive display)configured to output data to a user interacting with the electronicdevice, including one or more of the content data items. The electronicdevice may be in communication with the electronic device over a wiredor wireless based network connection, such as the Internet.

In a fourth aspect of this disclosure, there is provided a method forlinking transmedia content subsets formed of a plurality of transmediacontent data items and associated linking data which define time-orderedcontent links between the plurality of transmedia content data items,the transmedia content subsets comprising different groups of thetransmedia content data items and different content links therebetween,the method comprising:

-   -   associating the transmedia content data items with the        time-ordered content links and storing the linking data in the        memory; and    -   assigning the transmedia content data items to nodes of a graph        structure;    -   assigning the time-ordered content links to edges of the graph        structure; and    -   storing data pertaining to the nodes and edges in a transmedia        content model.

In a fifth aspect of this disclosure, there is provided a method forgenerating metadata for non-linearly connected transmedia contentcomprising a plurality of transmedia content data items and associatedlinking data which define time-ordered content links between theplurality of transmedia content data items, the plurality of transmediacontent data items being arranged into linked transmedia content subsetscomprising different groups of the transmedia content data items anddifferent content links therebetween, the method comprising:

-   -   generating, with a metadata generation engine, metadata        associated with a given subset of transmedia content data items        based on one or more metadata items associated with one or more        of the plurality of transmedia content data items within the        given subset.

In a sixth aspect of this disclosure, there is provided a computerreadable medium comprising computer executable instructions, which whenexecuted by a computer, cause the computer to perform the steps of theaforementioned methods.

BRIEF DESCRIPTION OF DRAWINGS

The present invention is described in exemplary embodiments below withreference to the accompanying drawings in which:

FIG. 1 depicts how users interact with content items according to thepresent disclosure.

FIGS. 2A, 2B and 2C depict a linear transmedia content subset, groupednon-linear transmedia content subsets, and a subset universerespectively, each formed of multiple transmedia content data items andtime-ordered content links.

FIG. 3 depicts the architecture of the system of the present disclosure.

FIG. 4 depicts an exemplary apparatus on which the back end of thepresent disclosure operates.

FIG. 5 depicts an exemplary apparatus on which the front end of thepresent disclosure operates.

FIG. 6 depicts exemplary non-linear networks of transmedia content dataitems according to the present disclosure.

FIG. 7A depicts an exemplary preference model component of therecommender engine component according to the present disclosure.

FIG. 7B depicts the evolution of a nonlinear network (story) over timeaccording to an embodiment of the present disclosure.

FIG. 8 depicts a user model component of the system.

FIG. 9 depicts a content modelling component of the system.

FIGS. 10A and 10B depict exemplary user interfaces that are rendered andoutput by the system.

FIG. 11 depicts a process for linking transmedia content subsetsaccording to the present disclosure.

FIG. 12 depicts a process for generating metadata for non-linearlyconnected transmedia content data items according to the presentdisclosure.

DETAILED DISCLOSURE

The present disclosure describes a new apparatus, system and method formanaging transmedia content. In one embodiment, there is disclosed aplatform for the creation, distribution and consumption of transmediacontent. The content may be arranged in a time-ordered manner forconsumption, thereby defining so-called “story” based content.

In the context of the present disclosure, groups of time-orderedcontent, for example in the form of stories, are made up of multipleelements of transmedia content, each being referred to herein as atransmedia content data items. Each item c can pertain to a narrativeelement of the story. Each transmedia content data item may be linked,and thus connected, to one or more other transmedia content data itemsin an ordered fashion such that a user can navigate through subsets ofthe transmedia content data items (also referred to as transmediacontent subsets) in a time-ordered fashion to consume some or all of anentire story.

The term “transmedia” means that the grouped content data items (whichare linked within the content subsets) comprise a plurality of differentmultimedia types, e.g. at least two different types of multimediacontent. For example, the different types of transmedia content data ofeach content data item within the subset can comprise at least twodifferent types from one or more of the following: textual data, imagedata, video data, audio data, animation data, graphical visualization orUI data, hypertext data, gaming data, interactive experience data,virtual reality (VR) data, augmented reality data, and multisensoryexperience data. Each transmedia content data item may itself comprisemultiple media types, e.g. video and audio data may be present within asingle item such that the audio is time-associated associated with thevideo.

The transmedia content data items can be grouped into transmedia contentsubsets. Each subset may be grouped based on one or more non-linearnetwork of the content data items.

Within each transmedia content subset, transmedia content data items arelinked to one another, directly or indirectly, by time-ordered linksbetween each data item. Typically, each time ordered-content link linkstwo transmedia content data items. An exception exists for a timeordered link which connects a subset entry point and a transmediacontent data item as explained below. The time-ordered link also definesa direction between the two transmedia content data items. The directionindicates an order in which linked transmedia content data items shouldbe presented to a user of the system. For example, when a firsttransmedia content data item is presented to a user, at least oneoutgoing time-ordered link (i.e. the direction defined by the link isaway from the first transmedia content data item) indicates a secondtransmedia content item that should be presented to the user next.

The term “subset” is used to denote a subset of transmedia content dataitems within the set of all transmedia content data items stored by thesystem. The transmedia content data items that are part of a transmediacontent subset are all directly or indirectly connected to each viatime-ordered content links between the transmedia content data items. Atransmedia content subset may be a linear, i.e. each transmedia contentdata item in the subset has at most one incoming time-ordered contentlink and one outgoing time-ordered content link, or a non-linearnetwork, i.e. one or more of the constituent transmedia content dataitems has more than one incoming or outgoing time-ordered content link.It will also be appreciated a non-linear network of transmedia contentdata items can be considered to be made up of multiple overlappinglinear paths from start-point to end-point through that network, andthat each linear path may also be considered to be a transmedia contentsubset. Furthermore, the term “group” has been used to denote acollection of transmedia content data items that are not necessarilyconnected, directly or indirectly, via time-ordered content links.However, where the term “group” has been used, a “subset” of transmediacontent data items, as defined above, may additionally be formed andutilised.

Each time-ordered link is stored in memory as link data comprising: theinput content data item; and the output content data item and thusimplicitly a direction between two content data items. Directional datafor the links is also stored which defines the path the links should beprocessed, and thus the order for processing of transmedia contentitems. The order can also be dependent on user interaction with eachitem as it is surfaced to a user.

Examples of transmedia content subsets are depicted in FIGS. 2A, 2B and2C. As mentioned above, the transmedia content data items are groupedinto transmedia content subsets.

In FIG. 2A, subset 100 defines a linear path of transmedia content dataitems 104 a-c. The subset 100 also comprises a subset entry point 102,which defines a starting point in the subset from which the system cancommence presenting the transmedia content data items within the subset.The subset entry point 102 may be linked to the first transmedia contentdata item 104 a by a time-ordered link, or may be a flag associated withthe first transmedia content data item 104 a which indicates that thetransmedia content data item 104 a is the first in the subset.

In the context of the present disclosure, the term “linear” means thateach transmedia content data item has, at most, only one incomingtime-ordered line (i.e. the direction defined by the link is inwardstowards the transmedia content data item) and only one outgoingtime-ordered link. The path defined by the transmedia content subset 100is unidirectional and there is only one possible route from the subsetentry point 102 and the final transmedia content data item of the path(i.e. a transmedia content data item with an incoming time-ordered linkbut no outgoing time ordered link).

The transmedia content data items may also be grouped based on multiplecontent subsets in a non-linear network, such as non-linear network 110depicted in FIG. 2B. In this context, the term “non-linear” means thattime-ordered links between the data items of each network may form aplurality of different paths through the network 110 which start indifferent places, end in different places, branch, split, diverge, leavesome data items out of the path and/or overlap with other paths. Such anon-linear network 110 can also be considered to be a group oftransmedia content subsets which share one or more transmedia contentdata items and/or time-ordered content links.

In the depicted non-linear network 110, each transmedia content dataitem 104 a-c, 112 a-b can have one or more incoming time-ordered linksand one or more outgoing time-ordered links. The data items 112 a, 104 band 112 b form a second transmedia content subset which shares the dataitem 104 b with the first transmedia content subset 100.

FIG. 2C depicts a story universe 120, in which multiple, relatednon-linear networks are grouped or clustered together. In oneembodiment, the non-linear networks of a story universe do not sharetransmedia content data items and/or time-ordered links with thenon-linear networks of another, different story universe. However, in analternative embodiment, the non-linear networks of a story universe doshare one or more transmedia content data items and/or time-orderedlinks with the non-linear networks of another, different story universe.

The system of the present disclosure manages the transmedia content dataitems, transmedia content subsets and one or more non-linear networks,facilitates the generation and manipulation of items between and withinsubsets and networks so that storylines can be formed. Accordingly, thecreation of transmedia content subsets and non-linear networks by a userof the system enables collaboration between users of the system andallows consumption of the created storylines. The architecture of thesystem is depicted in FIG. 3.

FIG. 3 depicts the overall architecture of the system 200. The system200 includes a front end device 210, which is typically located on auser device such as a smartphone, tablet or PC that is operated directlyby the user of the system 200, and a back end device 230, which istypically located on one or more servers that are connected to the userdevice via a network such as the Internet.

The back end 230 contains global resources and processes that aremanaged, stored and executed at a central location or severaldistributed locations. The front end 210 contains resources andprocesses that are stored and executed on an individual user device. Theback end 230 is responsible for tasks that operate on large amounts ofdata and across multiple users and stories, while the front end 210 onlyhas access to the resources of a particular user (or a group of users)and focuses on presentation and interaction.

The front end 210 communicates with the back end 230 via the network,the communication layer 212 that is part of the front end 210 and theexperience control layer 232 that is part of the back end 230. Theexperience control layer 232 is responsible for handling thedistribution of transmedia content data items, access limitations,security and privacy aspects, handling of inappropriate content dataitems, and user-specific limitations such as age group restrictions. Itensures that inappropriate, illegal, unlicensed or IP-violating contentis flagged and/or removed, either automatically, semi-automatically ormanually. It also handles sessions as the user interacts with the systemand provides session specific contextual information, including theuser's geolocation, consumption environment and consumption device,which can then be used by the front end 210 to adapt the consumptionexperience accordingly. The experience control layer 232 also acts as acheckpoint for content validation, story verification, and story logic,in order to provide users with a consistent story experience.

The communication layer 212 performs client-side checks on permissions,content validation, and session management. While the final checkshappen at the experience control layer 232 of the back end 230, theadditional checks carried out in the communication layer 212 help inproviding a consistent experience to the user (e.g. not displayingcontent or features that cannot be accessed).

The front end 210 also includes the user interface (UI) component 220,which is responsible for displaying and presenting the transmediacontent data items to users, including visual, auditory and textualrepresentations, and is also responsible for receiving the user's inputthrough pointing devices, touch events, text input devices, audiocommands, live video, or any other kind of interaction. The UI component218 can adapt to the user's location, environment, or current user statein order to provide an optimized experience.

The visual navigation component 214 is also included in the front end210, and allows a user to explore, browse, filter and search thetransmedia content data items, transmedia content subsets and non-linearnetworks, and any other content provided by the platform. For navigationin the context of transmedia content and stories, the visual navigationcomponent 214 provides intelligent abstractions and higher-levelclusterings of transmedia content data items, transmedia content subsetsand non-linear networks, providing the user with an interface forinteractive visual exploration of the transmedia content, which enablesthe user to make navigation choices at a higher-level abstraction beforeexploring lower levels, down to single stories, i.e. transmedia contentsubsets, and individual transmedia content data items. The structure oftransmedia content subsets and non-linear network and the time-orderedlinks between transmedia content data items data items is visualized aswell, providing the user with information on how these data items arerelated to each other. In one embodiment of this visualisation, a graphstructure is employed, with nodes representing transmedia content dataitems, and connections representing the time-ordered content links. Inthe main, the evolution of the transmedia content subsets and non-linearnetworks is rendered in real-time as the subsets and non-linear networksare created and modified by all users of the system. In addition, in aparticular embodiment which is user initiated, for example via userselection or automatically based on user interaction, e.g. immediatelyor shortly after a given user logs in to the system, the recent pastevolution of the transmedia content subsets and non-linear networks,e.g. the evolution since last login of the given user can be displayedgraphically, e.g. in a time lapse rendering of the changes of thetransmedia content subsets and non-linear networks in the order in whichthey occurred.

The social interaction component 216 handles visualisations and userinput related to interactions between individual users of the system200. It provides tools for editing a user's public information, enablingnotifications on other users creating content (following), endorsing andrating other users' content, and directly interacting with other usersthrough real-time messaging systems, discussion boards, and videoconferencing. These tools also allow users to collaboratively create newcontent (i.e. transmedia content data items) and author and edit stories(i.e. transmedia content subsets and/or non-linear networks), as well asdefine the access rights and permissions associated with suchcollaborative work. The enforcement of these rights is handled by theexperience control layer 232, as mentioned above.

In addition to the experience control layer 232, the back end 230comprises a user model component 234, a story model component 236, arecommender engine 238, and a metadata extraction component 240, inaddition to one or more data stores or computer memory for storing datarelated to the transmedia content such as the transmedia content dataitems, linking data, transmedia content subsets, non-linear networks,and metadata relating to the individual data items and linking data aswell as to the subsets and non-linear networks.

The user model component 234 represents user behaviour and properties.It is driven by a suite of algorithms and data collections, includingbut not limited to statistics, analytics and machine learning algorithmsoperating on user interaction patterns, consumption behaviour and socialinteractions. The analytics happens in real-time and the user modelcomponent 234 is continuously updated as the user interacts with thesystem 200. Additional information such as the user's geolocation can betaken into account as well. The corresponding data is stored in a userdatabase. The user model component 234 also comprises models for groupsof several users, which for example emerge during multiusercollaboration, consumption, geolocation, or social interactions. As partof the user model component 234, users and/or groups of users areprofiled and characterized according to their personality, productivitystage and other criteria. The user model component allows the system tomake predictions of user behaviour under real or hypotheticalconditions, which then feed into the recommender engine component 238.The user model component 234 also permits the correlation of interactionpatterns of users not identified to the system so as to re-identifyusers probabilistically.

The user model component 234 is also connected to the talent harvestcomponent, which, based on user behaviour, identifies individual usersor groups of users that fulfil certain criteria such as, for example,having a large amount of users consuming or endorsing their work, havingsignificant influence on other users' behaviours and opinions, or beinghighly popular personalities. The talent harvest component, in concertwith the recommender engine component 238, then influences the behaviourof such users of the system 200.

The story model component 236 is described in further detail below withreference to FIG. 7A. It characterises the content of single transmediacontent data items, transmedia content subsets, non-linear networks andwhole story universe, and stores the corresponding data in a story worlddatabase. The characterisations are found through algorithms such as,but not limited to, metadata extraction, analytics, graph analysis, orany other algorithms operating on connections between content ingeneral. Metadata extraction extends to include visual, auditory ortextual elements, as well as higher-level concepts like characters,character personality traits, actions, settings, and environments. Thecharacterisation also takes into account how users interact with thecontent, including the analysis of consumption patterns, content ratingsand content-related social interaction behaviour. The correspondingupdates of the story model component 236 happen in real-time as usersinteract with the system 200 or as new content is created or existingcontent is modified. Additionally, the story model component 236 makesuse of story, characterisations (including metadata) to model storylogic. Using story reasoning, the consistency of individual stories canbe verified and logical inconsistencies can be prevented either when astory is created or at the time of consumption. The story modelcomponent 236 is also in communication with the story harvest component,which uses the data provided by the story model component 236 in orderto identify and extract content (transmedia media content data items,transmedia content subsets, non-linear networks or higher-levelabstractions).

The recommender engine component 238 is in communication with both thestory model component 236 and the user model component 234 and providesconceptual connections between the story model component 236 and theuser model component 234. The recommender engine component 238 uses dataanalytics to match content (transmedia content data items, transmediacontent subsets, non-linear networks) with users, suggesting users forcollaboration, suggesting content to be consumed, or suggesting stories,story arcs or story systems to be extended with new content. It alsotakes into consideration products and brands as a third dimension,resulting in an optimization in the three-dimensional user-story-brandspace. Recommendations can be explicit, with recommendations beingexplicitly labelled as such to the user, guiding the user through atransmedia content subset or non-linear by providing an optimalconsumption path or suggesting other users to collaborate with, or theycan be implicit, meaning the user's choice is biased towards certainelements of content (including transmedia content, advertisement,users), without making the bias explicitly visible to the user.

The metadata extraction component extracts metadata from transmediacontent (i.e. transmedia content data items, transmedia content subsetsand/or non-linear networks) automatically, semi-automatically, ormanually. The metadata extraction component 240 tags and annotatestransmedia content, providing a semantic abstraction not only of thecontent of individual transmedia content data items, but also of thetime-ordered links, transmedia content subsets, non-linear networks, andstory systems. The derived metadata thus spans a horizontal dimension(cross-domain, covering different types of media) as well as a verticalone (from single transmedia content data items to whole story systems).

Also depicted in FIG. 3 are story world database 280, media (content)database 282 and user database 284. The databases 280-284 are stored inmemory 301 of server device 230. The story world database 280 storesdata characterising and defining the structure of the transmedia contentsubsets, non-linear networks and whole story systems, for example by wayof linking data defining the subset structure. Additionally, metadataand graph analytics pertaining to the subsets and networks may also bestored in the story world database 280. The media database 282 storesindividual content items, and data characterising the content ofindividual transmedia content data items, e.g. metadata and graphanalytics for the individual content items. The user database 284 storesuser data pertaining to users of the system 200, including userbehaviour data defining how users have interacted with individualcontent items and subsets, and user preference data defining userindicated or derived preferences for content items and subsets.

FIG. 4 depicts an exemplary back end server device 230 on which the backend of system 200 is implemented. It will be appreciated that the backend or functional components thereof may be implemented across severalservers or other devices. The server device 230 includes the memory 301,processing circuitry 302 and a network interface 303. The memory may beany combination of one or more databases, other long-term storage suchas a hard disk drive or solid state drive, or RAM. As described above,the memory 301 stores the transmedia content data items and associatedlinking data, which define time-ordered content links between theplurality of transmedia content data items. The plurality of transmediacontent data items are arranged into linked transmedia content subsetscomprising different groups of the transmedia content data items anddifferent content links therebetween. The processing circuitry 302 is incommunication with the memory 301 and is configured to receiveinstructions from a user device via the network interface to create newtime-ordered content links between at least two of the plurality oftransmedia content data items and modify 301 the linking data stored inthe memory to include the new time-ordered content link.

FIG. 5 depicts an exemplary user device 210 on which the front end 210of system 200 is provisioned. The user device 210 includes a memory 401,processing circuitry 402, network interface 403 and a user interface404. The user interface 404 may comprise one or more of: atouch-sensitive input, such as a touch-sensitive display, a touchscreen,pointing device, keyboard, display device, audio output device, and atablet/stylus. The network interface 403 may be in wired or wirelesscommunication with a network such as the Internet and, ultimately, theserver device 230 depicted in FIG. 4. The electronic device 210 receivesuser input at the user interface 404 and thereby communicates with theserver device 230 via the network interface 403 and network interface303, which provides the processor 302 with instructions to create newtime-ordered content links between the transmedia content data items inthe memory 301. The electronic device 210 may also provide instructionsto the server device 230 to delete or modify existing time-orderedcontent links and/or transmedia content data items from the memory.

It will be appreciated that the system may comprise multiple electronicfront end devices 210, each configured to receive user input and therebycommunicate with the server device 230 and provide instructions tocreate, delete or modify time-ordered content links between thetransmedia content data items. Thus, multiple electronic devices 210,each being accessed by a different user, are adapted to process commoncontent links and content data items which are accessible to The memory301 of the server device 230 may also store user data items, which areassociated with users of the system 200 and comprise user identificationdata, such as a username, password, email address, telephone number andother profile information. The user data items may also comprise, foreach user of the system user preference data pertaining to each user'spreferences, user behaviour data pertaining to the each user's onlinebehaviours, user interaction data pertaining to the each user'sinteraction with other users, and/or user location data pertaining tothe current determined and/or past determined location of the each user.

The server device 230 may also be configured to implement the user model234 of the system 200 as mentioned above. The processing circuitry 302of the device 230 can use the user model 234 to identify userinteractions of the users of the system 200 with the transmedia contentdata items and subsequently update the user interaction data stored inthe memory 301 in accordance with the user interaction.

The memory 301 may also store content characterisation data items, whichcharacterise one or more of the transmedia content data items. Inparticular, the memory 301 may store auditory characterisation datawhich characterises auditory components the transmedia content dataitems, visual characterisation data which characterises visualcomponents of the transmedia content data items, textualcharacterisation data which characterises textual components of thetransmedia content data items and/or interaction characterisation datawhich characterises interactive components, such as games, or quizzes,or puzzles, of the one or more transmedia content data items. Theprocessing circuitry 303 of the server device 230 can be furtherconfigured to provide a content subset modelling engine that processesthe content characterisation data items for each transmedia content dataitem in a given transmedia content subset and generates unique subsetcharacterisation data for the transmedia content subset based on theprocessed characterisation data. The content subset modelling engine maybe provided by the story model component 236 mentioned above.

The processing circuitry 302 may also implement the transmedia contentrecommender engine 238, mentioned above, which is configured to processthe characterisation data items and the user data items for a given userand identify transmedia content data items and surface identification(s)of the transmedia content data items that are predicted to be matched tousers, and additionally can surface identification(s) of other matchedusers of the system 200.

The processing circuitry 302 of the server device may also be configuredto implement the experience control layer 232 mentioned above. Theexperience control layer 232 implements a permission control systemwhich is used to determine whether a given user has permission to view,edit, modify or delete a given transmedia content data item,time-ordered content like, transmedia content subset or non-linearnetwork. Collaboration is a challenge in itself; however, authorshipattribution and consistency in particular are supported. A balance isthen provided between a very rigid and tight permission system, whichmight hinder collaboration and discourage potential contributors fromsharing their ideas, and an open system which allows any user to modifyor delete the content contributed by other users.

For a given transmedia content subset or non-linear network, created byan original first user (referenced hereinafter as “Alice”), and whichconsists of individual transmedia content data items connected bytime-ordered content links therebetween, this transmedia content subsetor non-linear network is attributed to and owned by Alice in metadataassociated with the transmedia content data items, linking data and theoriginal transmedia content subset and/or non-linear networkexclusively. The experience control layer 232 only allows modificationsto the original transmedia content subset or non-linear network byAlice. The system 200 is also configured such that a systemadministrator user or moderator user can provide or change permissionsfor and between all individual users, regardless of permissions assignedby individual users.

A second user (referenced hereinafter as “Bob”) may wish to contributeto the transmedia content subset or non-linear network. Bob may wish toinsert a new transmedia content data item into the transmedia contentsubset or non-linear network, and/or Bob may wish to create new linkingdata that defines an alternative path through the non-linear network ortransmedia content subset.

The experience control layer 232 does not permit Bob to modify theoriginal transmedia content subset or non-linear network that areattributed to and owned by Alice in the metadata. Instead, theexperience control layer 232 instructs the processor 302 to create acopy of the original transmedia content subset or non-linear network inthe memory 301, which includes the changes to the transmedia contentdata items and/or linking data input by Bob.

The copy will be exclusively owned by Bob in the metadata associatedwith the copied transmedia content subset or non-linear network, and assuch the experience control layer 232 will permit only Bob to edit ormodify the copied transmedia content subset or non-linear network.However, the original transmedia content data items and linking datacontributed by Alice remain attributed to Alice in the metadata, andonly the new transmedia content data items and linking data areattributed to Bob in the metadata. The copied transmedia content subsetor non-linear network maintains a reference to the original transmediacontent subset or non-linear network.

As Bob interacts with the content by creating, modifying and editingcontent items, subsets and non-linear networks, it updates in real timesuch that all other users can see the changes as they happen, includingAlice.

In an alternative embodiment, Bob can interact with the content bycreating, modifying and editing content items, subsets and non-linearnetworks so that the changes at this stage can only be seen by Bob. WhenBob is finished and no longer wishes to modify the content, subsets ornon-linear networks, he can formally submit his changes and theexperience control layer 232 provides the copied transmedia contentsubset or non-linear network to the user, more particularly to the userdevice of the user that is indicated by the metadata of the originaltransmedia content subset or non-linear network as the owner, e.g.Alice's user device, for review. Alice may then choose to “merge” thecopied transmedia content subset or non-linear network with theoriginal. The experience control layer 232 will delete the original andmodify the metadata of the copy to indicate that Alice is owner of thecopied transmedia content subset or non-linear network, since Bob willhave previously been designated in the metadata as owner of any items,subsets or networks which were created or modified by him. The metadataof the individual transmedia content items and linking data, other thanthe owner, is left unchanged.

Alice may approve of the modifications made by Bob, but may wish keepthe modifications as an optional alternative. In this case, she willchoose to “branch” the original transmedia content subset or non-linearnetwork. The experience control layer 232 will modify the originaltransmedia content subset or non-linear network to include any newtransmedia content data items and linking data contributed by Bob. Themetadata of the individual transmedia content items and linking data isunchanged, and the metadata for the modified original transmedia contentsubset or non-linear network still identifies Alice as the owner.

Finally, Alice may disapprove of the modification made by Bob, and canchoose to “push out” Bob's version. This causes the experience controllayer 232 to remove the reference link from the copy to the original.Again, the metadata of the individual transmedia content items andlinking data is unchanged. This means that Bob's version of the contentitems, subset and non-linear networks as a result of his creation and/ormodifications are now distinct from Alice's, and exists separately inmemory with corresponding metadata to indicate that he is the owner ofthis version.

The system 200 and experience control layer 232 may allow Alice to fullydelete Bob's edit, or force the modification to be hidden from otherusers of the system. Allowing for this option might be required in somecases, for example when copyright is infringed or the contentcontributed by Bob is inappropriate.

As mentioned above, the system 200 structures groups of content subsets(“storyworlds”), i.e. non-linear networks, as directed graphs ofconnected transmedia story data items and storylines, i.e. transmediacontent subsets, as sequences of connected transmedia content data itemsout of the generated non-linear networks graphs.

The story model component 236 of the system 200 arranges the stories andstoryworlds at the transmedia content level based on complex graphmetrics. The transmedia content data items are nodes. Edges of the graphdefine the time-ordered content links between transmedia content dataitems. The edges and nodes of the graph may be assigned with weightsderived from story attributes, e.g. number of likes received by usersconsuming the story. The graph-based model defines all of the possibleconsumption flows throughout a given graph and allows identification oftransmedia content data items which play key roles within thestoryworld. FIG. 6 depicts a graph-based model of two non-linearnetworks of transmedia content data items.

Each depicted non-linear network, 510 and 520 includes at least twosubset entry points 511 and 521 which define starting points in thesubset (and also any non-linear networks that the subsets are part of)from which the system should begin presenting the transmedia contentdata items. Non-linear network 511 has two distinct end points 513, 516,which are transmedia content data items that have only incomingtime-ordered content links. End point 513 is preceded by a branch 512 ofthe non-linear network, which shares only its first data item in commonwith a second brand 514. Branch 514 has an alternate path 515 whichskips several of the transmedia content data items of the rest of thebranch and then re-joins the branch 515 to terminate at the end point516. In contrast, non-linear network 520 has four branches with fourdistinct end points 523, 525, 526 and 527, which share varying numbersof transmedia content data items in common with one another. The datathus generated and stored which is representative of the non-linearnetwork is structural data indicative of the nodes and edges and linkstherebetween, thereby defining the time-ordered structure of contentdata items in for the non-linear network.

The story model component 236 provides the graph model of the transmediacontent described above. The story model component 236 models eachnon-linear network as a triple W=(G, E, Y), where G=(M, L) is a weighteddirected graph of n vertices, E={e₁, . . . , e_(i)} is a set of subsetentry points to the non-linear network, and Y={y₁, . . . , y_(k)} is aset of linear paths (“yarns”), where 1<=i<=k. M={m₁, . . . , m_(n)} is aset of transmedia content data items (i.e. the vertices of the graph)and L is a set of directed edges connecting the transmedia content dataitems. Each linear path y=<m_(j)>_(1<=j<=n) is a sequence of consecutiveconnected transmedia content data items. Subset entry points point to orare transmedia content data items and the first access point to consumethe transmedia content in a non-linear network. Each graph may haveseveral entry points. If E is zero, there are no entry-points to thegraph, making it inaccessible to users of the system 200. A non-linearnetwork may be made of a single linear path or a composition of severallinear paths. It begins from an entry-point e and ends with the lasttransmedia content data item of a linear path y_(x). Namely, each paththroughout the graph a user can take while consuming a sequence of mediaelements is a story. P(e,y_(x)) is defined as a set of paths startingfrom e and ending at y_(x). Given a story world W, an entry-point e, anda linear path y_(x), the function S_(W)(e,y_(x)) presents all of thestories that can be extracted from e to y_(x). If the entry-point e isin linear path y_(x), then:

S _(W)(e,y _(x))=y ₁ ^(e).

This expresses a story made of a single linear path, which is theshortest form of story created by a user. All of the stories created ina story graph from any entry-point e to any linear path y_(x) is definedby:

S(W)=S _(∀eεE,yεY) S _(W)(e,y)

(Ø,@P(e,y _(x))

S _(W)(e,y _(x))=y ₁ ^(e) ,x=1,eεy _(x)

y ₁ ^(e) ∘ . . . ∘y _(x) ,x>1,e6εy _(x) ,∃P(e,y _(x))

The set of outgoing and incoming neighbours of a transmedia content dataitem mεM are denoted by L⁺(m) and L⁻(m), respectively. Let d(e,m) be theshortest distance from e to m. If there is no path from e to m, d(e,m)is considered to be ∞. For two transmedia content data items e and m,P(e,m) is the set of transmedia content data items on at least one ofthe shortest paths from e to m, that is,P(e,m)={νεV|d(e,ν)+d(ν,m)=d(e,m)}.

The story model component 236 uses the graph description provided aboveto provide various measures of the non-linear network described by thegraph. Graph centrality measures have attracted considerable attentionfor studying various kinds of network data structure. Centralitymeasures can be used to identify important nodes for many applications.Depending on the use-case scenario, the importance of a node can havedifferent meanings. In the graphs used by the story model component 236,the centrality of transmedia content data items, or the identificationof which transmedia content data items are more central than others, isinterpreted as the importance of the transmedia content data items.

The degree of a node is the number of links incident to the vertex,which is the cardinality of the node's neighbourhood. The degree of anode m in a graph M is denoted d_(m)(M). The in-degree and out-degree ofa node in a directed graph is the number of incoming |L⁺(m)| andoutgoing |L⁻(m)| links, respectively. The transmedia content data itemdegree shows the non-linearity level of the path through the network atthat point. The majority of scenes express a linear behaviour in the waystories are built. A media element is isolated if its degree is 0. Afundamental characteristic of a graph is its degree distribution. Thedegree distribution of a graph is a description of the relativefrequencies of nodes that have different degrees. That is, P(d) is thefrequency distribution of nodes that have degree d. The density,

$D = \frac{L}{{M}\left( {{M} - 1} \right)}$

of a graph keeps track of the relative fraction of links.

The degree centrality, the simplest yet the most popular measure of theposition of a given node in a graph. The degree centrality of a node mis defined by:

${C_{D}(m)} = \frac{d_{m}(M)}{\left( {n - 1} \right)}$

and indicates how well a node is connected in terms of directconnections.

Closeness centrality measures how close a given node is to any othernode. It is defined as the inverse of the average shortest distancebetween i and any other node

$m,{{C_{i}^{C}(M)} = \frac{\left( {n - 1} \right)}{\sum\limits_{m \neq i}{l\left( {i,m} \right)}}},$

where l(i,m) is the number of links in the shortest path between i and min the graph M. It is 0 if a node is isolates, and 1 if it is directlyconnected to all of the other nodes

A structural property of each transmedia content data item in a graph ishow many story paths between transmedia content data items pass throughthem. Betweenness centrality, can be used to capture this structuralproperty. Betweenness centrality measures how well situated a node is interms of the paths that it lies on. That is, how important a node is interms of connecting other nodes. Let ν_(e,m) be the number of shortestpaths between nodes e and m, and let ν_(e,m)(i) denote the number ofshortest paths between e and m that i lies on. It can be estimated howimportant i is in terms of connecting e and m by looking at the ratio:

$\frac{\sigma_{e,m}(i)}{\sigma_{e,m}}.$

That is, if it is close to 1, then i lies on most of the shortest pathsconnecting e and m, while if it is close to 0, then i is less criticalto e and m. Averaging across all pairs of nodes, the betweennesscentrality of a node i in a graph M is

${C_{i}^{B}(M)} = {\sum\limits_{{e \neq m},{i \notin e},m}\frac{{\sigma_{e,m}(i)}/\sigma_{e,m}}{\left( {n - 1} \right)\left( {n - 2} \right)}}$

The story model component 236 also uses a physical state model analogyto provide measures of entropy, temperature, volume and pressure fornon-linear networks. Entropy (S), as a measure of disorder of thenon-linear network. Networks with disconnected or unstable paths throughthe network show high entropy values and cab be defines as being in a“gas” state. Networks which have multiple nodes and edges in commonamong the different linear paths in the network have a low entropy, suchthat a “liquid” state can be defined for the non-linear network having alower entropy than the “gas” state and “solid” networks, lower entropiesstill.

Temperature (T) is a measure of hot or cold individual transmediacontent data items, transmedia content subsets, or non-linear networksare, and is measuring according to the number of shares, likes, or otherendorsements that each item receives from users of the system 200.

Volume (V) is defined as the number of transmedia content data items ina given transmedia content subset or non-linear network.

Pressure is defined as the temperature T over the volume V at differentlevels of abstraction.

$p = {{V\underset{\_}{T}} = {{\sum{\frac{{{shared}\left( {x_{v},{{{{otxl}{()}}x} +},{tlxi}} \right)}{{ked}\left( {x,{tx}} \right)}}{x \in \Delta}\mspace{14mu} {where}\mspace{14mu} t}} \in \Gamma}}$

Γ is the set of media types, Δ is the set of media elements in the storysystem. If a given quantity of media elements expresses a constantpressure, then its volume is directly proportional to the temperature.

$\frac{V_{1}}{T_{1}} = \frac{V_{2}}{T_{2}}$

This implies that two or more story systems (story bundles, countries,continents) with different volumes, and proportional temperatures,express the same pressure.

With reference to FIG. 7, it can be seen how the story model component236 communicates with other internal and external components or modulesof the system 200 which are explained in further detail below.

The story world representation model component 271 fetches linkedtransmedia content data and provides graph-based knowledgerepresentations of linked subsets previously generated by users andstored in the memory 301 of the headend device 230. It shows howtransmedia content items are connected at different levels ofabstraction, e.g. content items (story element), content subsets asnetworks (story), country, continent, story system. Representations canbe also provided at different points in time and with varyinggranularity. The story world representation model 271 can provide morefine-grained representations by querying the following components whichare described in further detail below, namely: user profile module 272,user behaviour module 273, and content profile module 274.

The user profile module 272 is configured to provide detailed profilesof users, including demographics (e.g. age, gender, etc.), personalitytraits (e.g. Big 5) and localization (for example determined by alocation determination module in the user device 210, such as a GPSreceiver) over time, by querying the user model 234.

The user behaviour module 273 is configured to provide detailedinformation of the behaviour of individual users and associated groupsof users (e.g. communities) at different levels of abstraction and at acertain point in time by querying the user model 234. User dynamics areof interest at different levels of granularity: single users, smallgroups, audiences. This multidimensional prediction provides insightsinto dynamic behaviour for load balancing, information about the mostlikely story path of a user (e.g. for branded content insertion) andenables prediction for the evolvement of the whole system.

The content profile module 274 is configured to fetch metadatainformation by means of the metadata extraction component 240 andcontent information from the memory 301. It is configured to provide adetailed profile, e.g. media type, creation date, genre, authors, ofeach single media element, story, country, continent and story system,respectively. Metadata extraction can be extended to include visual,auditory or textual elements, as well as higher level concepts such ascharacters, character personality traits, actions, settings, andenvironments.

An interaction model component 275 is configured to receive graph-basedrepresentations from the story world representation model component 271and can provide interaction activities of transmedia content items withother items (e.g. how the items are connected in the graph-based model)at all of the levels of the story system, during a specific interval oftime, and with different levels of granularity. The analysis andprediction of story interactions are used to foster collaborationsacross all levels of the system 200, to predict the quality of storiesand to influence story interconnections. A content profile for each itemcan be taken into account to provide analysis with a higher level ofdetail; for example, content interconnection by genre or media type. Theinteraction model component 275 can request story world representationsbased on a specific user profile, a defined user behaviour, and aparticular content profile.

The story evolution model component 276 is configured to take input fromthe interaction model component 275 and derive and store data pertainingto how the non-linear networks 510, 520 (stories) develop through time,with varying time granularity. Such an evolution is depicted in FIG. 7B.It is further configured to make predictions of future network evolutionat all of the levels of the story system. The component 276 can providemore specific analyses by taking into account metadata information, userbehaviour and related profiles. By using this information and thegraph-based algorithms set out above, the model can predict the contentconsumption (e.g. popularity of content), the story evolution (e.g. ifcontent subsets will grow or shrink), and its state (i.e. solid, liquidor gas). Predicting the state of stories helps to make a betterassessment for content surfacing to users, for example, surfacing onlycontent items and/or subsets which are wholly in a solid story system.

The state model component 278 takes input from the story worldrepresentation model 271 and the interaction model 275 and derives astate of the story system, which can be solid, liquid or gas. It isfurther configured to extract measurements to characterize the storyworld at all levels of abstraction (i.e. the three states mentionedabove). Modelling and characterizing the state of the system can helpidentify and extract relevant content items and subsets thereof, and todrive recommendations with the recommender engine 238 being providedwith input from the state model component 278, so as to foster creationof story elements and provide accurate predictions of storydevelopments. The state model component 278 is able to derive andpredict the evolution of the story state and its entropy. The storystate and actions can be represented by a Hidden Markov Model, where thelatent variable of such a model represents the state of the storysystem.

The story model component 236 can also uses the measures defined aboveto characterise and harvest storylines from within the linked transmediacontent data items. This is achieved by way of a harvesting engine 252.

The harvesting engine 252 in communication with the story modelcomponent 236 identifies a number of common nodes and/or a number ofcommon edges shared between at least two of the linked transmediacontent subsets and extract from the memory one or more linkedtransmedia content subsets that share common edges and/or nodes when thenumber of common edges and/or nodes exceeds a pre-defined threshold.

The harvesting engine 252 can extract the one or more linked transmediacontent subsets by copying the transmedia content data items andassociated linking data of the extracted transmedia content subset toanother region in the memory 301, or may store a reference to eachtransmedia content data items and each item of associated linking dataof the extracted transmedia content subset in the memory 301.

The harvesting engine 252 can also take into account metadata associatedwith the non-linear networks and transmedia content subsets to determinewhether a given storyline or linear path through a non-linear network iscomplete, i.e. whether the story has an ending, before harvesting.

The metadata extraction component 240 includes a metadata generationengine which generates metadata associated with a given subset oftransmedia content data items based on one or more of the metadata itemsassociated with the transmedia content data items comprised within thegiven subset by grouping the metadata associated with the transmediacontent data items of the transmedia content subset and copying thegrouped metadata to another region in the memory or copying referencesto the grouped metadata to another region in the memory.

In a hierarchical system containing a lot of changing content atdifferent levels (story systems, non-linear networks, transmedia contentsubsets and individual transmedia content data items) users can easilyget lost in irrelevant content (for the user) or unappealing content(for the user). An engine guiding the user and fostering the creation ofhigh quality content is thus provided.

The metadata generation engine may also generate metadata by analysingtransmedia content data items (audio, video, text, images, games, etc.)or graph structures automatically, and/or from input received at a userinput interface.

The metadata generation engine also includes a metadata aggregator,which can receive metadata from one or more sources, analyses thereceived metadata and associates the analysed metadata with thetransmedia content data items and transmedia content subsets byanalysing the metadata received from the plurality of sources todetermine metadata tags to associate with the transmedia content dataitems and transmedia content subsets

As mentioned above, the system 200 further includes a recommender enginecomponent 238. Users of the system, through the recommender enginecomponent 238, receive suggestions about possible story elements, i.e.transmedia content data items, to be consumed and/or extended with newcontent. Due to the hierarchic nature of the system, recommendations areissued at different levels of granularity, e.g. story system, non-linearnetworks, transmedia content subsets and individual transmedia contentdata items. Furthermore, recommendations are dynamic, i.e. they changewith continuously evolving content. Recommendations also take intoaccount preferences of the user to keep the user engaged with processingthe time-arranged content items. This can mean that individual contentitems from the same or other users with sufficiently similar oridentical content characteristic metadata, e.g. specifying content withsufficiently similar characters, occurring at sufficiently similar oridentical factual or fictional times, or in sufficiently similar oridentical factual or fictional times, as the characteristic metadata ofthe content which has already been utilised (consumed) by the user canbe surfaced by the recommender engine component 238.

The recommender engine component 238 is configured to access the memory301 of the server device 230 and surface one or more individual contentitems and/or linked transmedia content subsets to a user of the system.The surfaced content items or linked transmedia content subsets arechosen by the recommender engine component from the individualtransmedia content data items and transmedia content subsets stored inthe memory 301. In the present context, “surface” means that theselected one or more item is/are isolated from other items of contentand provided to the user, e.g. as a notification on the user device, oras a special flag associated with the surfaced item.

The recommender engine 238 component may also include a preference model600 that provides a predicted rating of a given transmedia content dataitem or transmedia content subset for a specific user of the system 200.The preference model 600 is depicted in FIG. 8. The preference model 600takes as input one or more transmedia content data items or transmediacontent subsets and provides as output predicted rating for each inputitem for a given user. The preference model 600 achieves this by, at afirst step 601, removing global effects. Some users might, for example,tend to constantly give lower ratings than others. Removing this biasbefore processing the input items improves prediction accuracy. In asecond step 602, the model collects the predictions of n independentstate of the art algorithms (such as Rank-based SVD). The system thenbuilds an ensemble prediction at step 603 by using a Lasso Regression.In the last step 604, the global effects are added back to the ensembleprediction to obtain the final rating (or score) 605 for the given user.

The recommender engine may also include a user-brand match component,which is configured to provide, for a given user, a prediction of apreference for a given branded content data item, and a branded contentmodel that provides, for a given transmedia content data item, aprediction of the suitability of a given branded content data item, e.g.an advertisement.

The transmedia content recommender engine is configured to query thepreference model, user-brand match component and brand model componentby providing the preference model, user-brand match component and brandmodel with a transmedia content parameter, user data for the given userand a given branded content data item, and to maximise the sum of theoutput for the preference model, user-brand match component and brandmodel over the transmedia content parameter. This three-dimensionaloptimisation ensures that users are engaged by relevant, while consumingcontent containing advertisements of a desired brand.

The transmedia content recommender engine is configured to surface thetransmedia content data item or transmedia content subset that has themaximum output of the three-dimensional optimisation.

The recommender engine component 238 may also take into account a givenuser's defined preferences, and other predicted properties such as userbehaviour, or emotional state. In order to achieve this, the recommenderengine component 238 communicates with the user model component 234depicted in FIG. 3 and shown in more detail in FIG. 9. The user modelcomponent 234 includes a state model 701, behaviour model 702, userprofile 703, interaction model 704 and talent harvest component 705.

Modelling the state of the user, using state model component 701 permitspersonalised recommendations to be provided by the recommender enginecomponent 238, and also provides accurate predictions of user behaviourby the behaviour model component 702. The state model component 701 mayalso be used to customise the user interface 218 and encourage users tocreate new content, i.e. new transmedia content data items andtime-ordered content links, within the system 200. The state modelcomponent 701 represents and predicts the continuous creational,emotional and affective state of users of the system 200. The creationalstate describes if the user is in the mood for consuming transmediacontent subsets or non-linear networks or contributing their owntransmedia content data items and time-ordered content links. Theaffective state indicates whether the user is, for example, motivated orbored. The emotional state describes the emotions that individualtransmedia content data items or transmedia content subsets/non-linearnetworks trigger in the user.

Due to the hierarchy of the system, i.e. the logical separation oftransmedia content into levels of the individual transmedia content dataitems, transmedia content subsets, non-linear networks and storyuniverse, user behaviour is predicted at different levels of granularityin two main dimensions, namely: (1) the transmedia content hierarchy;and (2) the interaction of users with other users. User dynamics are ofinterest at different levels of user granularity, for example in respectof: single users, small groups, audiences. The behaviour model component702 predicts the user behaviour in both of these dimensions and providesinsights into dynamic behaviour for load balancing, information aboutthe most likely path of a user through a given non-linear network andpredicts the evolution of the whole system 200.

The user preference component 703 provides a detailed profiling for eachuser of the system 200, including, for example, demographics (e.g. age,gender, etc.), personality traits (e.g. Big 5) and location data (GPS).

The interaction model component 704 monitors and predicts socialinteractions of users. The analysis and prediction of socialinteractions of groups of contributors can be used by the system 200 toencourage collaboration across all levels of the system, predict thequality of stories and influence user community building.

Talent harvest component 705 identifies users of the system 200 withexceptional talent at creating transmedia content, and categorises theseusers according to their type of talent, e.g. a Star Wars expert, anartist doing funny comic strips or a user resonating with a specificaudience.

As shown in FIG. 9, the user model component 234 is in communicationwith the story model component 236, the recommender engine component 238and the metadata extraction component 240, with these components beingboth inputs and outputs to the user model component 234, allowing dataexchange therebetween.

Navigating through a large quantity of transmedia content data items,transmedia content subsets and non-linear networks that are provided tousers by the system 200, in a way that users and user groups can createand consume the data quickly on a range of devices, including personalcomputers, laptops, tablets, mobile devices etc. is challenging. Theuser interface component 218 guides the user in a non-disruptive way,whilst also avoiding repetitive meandering and visual overload ofcontent creation and navigation tools on the multiple, hierarchicallevels of the transmedia content.

The user interface component 218 presents the transmedia content dataitems, transmedia content subsets and non-linear networks as follows. Athree-dimensional representation is utilised based on one or morethree-dimensional shapes which can be manipulated by the user. In atwo-dimensional system involving a two-dimensional display screen, atwo-dimensional representation of the three-dimensional shape(s) is/aregenerated, and the shape(s) utilised may be one or more spheres orellipsoids, which use hierarchically-connected visual metaphors toprovide further information on the transmedia content data items and howthey are related and connected in a time-based manner for users andgroups of users. This is achieved in a non-disruptive andnon-distracting manner. It will be appreciated that anythree-dimensional object may be used to present the transmedia content.In one embodiment of the present invention, the visual metaphors canequate to features of a planet, such as continental landmasses, oceans,clouds, mountain ranges and coastlines.

FIGS. 10A and 10B depict an example of the user interface that ispresented to a user of the system 200 at different levels of thehierarchical tree structure. FIG. 10A depicts a higher-level view of thetransmedia content 800 which depicts several spheres 801, 802. Eachsphere 801, 802 represents a storyworld, i.e. groups of transmediacontent subsets and non-linear networks that are semantically similar,e.g. the constituent transmedia content data items relate to the samestory characters or story universe. The spheres themselves may bevisually clustered together in the displayed three-dimensionalrepresentation according to semantic similarity between the storyworlds.

A user may select one of the spheres 801, 802, which causes the userinterface 800 to transition to a modified user interface 810, whichdepicts the selected single sphere 811 with additional detail.Additional surface features of the selected sphere 811 are displayed inuser interface 810, such as individual transmedia content subsets ornon-linear networks indicated by icons 813, and representative images ofthe content 814. The visual metaphors are provided such thatsemantically similar transmedia content subsets and non-linear networksare depicted on the same continents 812 on the surface of the planet811. When a user wishes to consume or edit an individual transmediacontent subset or non-linear network, the user can select one of theicons 813 or images 814 and the user interface 810 transitions to show agraph-structure of the subset/network and/or the individual transmediacontent data items.

FIG. 11 depicts a method 1100 for linking transmedia content subsetsformed of transmedia content data items and their associated linkingdata which define the time-ordered content links between the transmediacontent data items. At step 1101, the processing circuitry 302 of theserver device 230 associates the transmedia content data items with thetime-ordered content links and stores the linking data in the memory 301of the server device 230. At step 1102, the processing circuitry 302assigns the transmedia content data items to nodes of a graph structure,and at step 1103 assigns the time-ordered content links to edges of thegraph structure. The data pertaining to the nodes and edges of the graphis then stored in the memory 301 as part of a transmedia content model.

FIG. 12 depicts a method 1200 for generating metadata for non-linearlyconnected transmedia content data items and their associated linkingdata which define time-ordered content links between transmedia contentdata items. The transmedia content data items are arranged into linkedtransmedia content subsets made up of different groups of the transmediacontent data items and different content links therebetween, and arestored in the memory 301 of the server device 230. At step 1201 theprocessing circuitry 302 of the server device 230 generates, using ametadata generation engine, metadata that is associated with a givensubset of the transmedia content data items stored in the memory 301based on one or more metadata items associated with one or more of thetransmedia content data items within the given subset.

While some exemplary embodiments of the present disclosure have beenshown in the drawings and described herein, it will be appreciated thatthe methods described herein may be deployed in part or in whole througha computing apparatus that executes computer software, program codes,and/or instructions on processing circuitry, which may be implemented byor on one or more discrete processors. As a result, the claimedelectronic device, apparatus and system can be implemented via computersoftware being executed by the processing circuitry. The presentdisclosure may be implemented as a method in a system, or on anapparatus or electronic device, as part of or in relation to theapparatus or device, or as a computer program product embodied in acomputer readable medium executable on one or more apparatuses orelectronic devices.

A processor as disclosed herein may be any kind of computational orprocessing device capable of executing program instructions, codes,binary instructions and the like. The processor may be or may include asignal processor, digital processor, embedded processor, microprocessoror any variant such as a co-processor (math co-processor, graphicco-processor, communication co-processor and the like) and the like thatmay directly or indirectly facilitate execution of program code orprogram instructions stored thereon. Each processor may be realized asone or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. A givenprocessor may also, or instead, be embodied as an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. In addition, each processor may enableexecution of multiple programs, threads, and codes. The threads may beexecuted simultaneously to enhance the performance of the processor andto facilitate simultaneous operations of the application. By way ofimplementation, the methods, program codes, program instructions and thelike described herein may be implemented in one or more thread. Thethread may spawn other threads that may have assigned prioritiesassociated with them; the processor may execute these threads based onpriority or any other order based on instructions provided in theprogram code.

Each processor may access one or more memories, for example one or morenon-transitory storage media which store the software, executable code,and instructions as described and claimed herein. A storage mediumassociated with the processor for storing methods, programs, codes,program instructions or other type of instructions capable of beingexecuted by the computing or processing device may include but may notbe limited to one or more of a CD-ROM, DVD, memory, hard disk, flashdrive, RAM, ROM, cache and the like.

The methods and/or processes disclosed herein, and steps associatedtherewith, may be realized in hardware, software or a combination ofhardware and software suitable for a particular application. Thehardware may include a general purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, the methods described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in a system that performs the stepsthereof, and may be distributed across electronic devices in a number ofways, or all of the functionality may be integrated into a dedicated,standalone electronic device or other hardware. In another aspect, themeans for performing the steps associated with the processes describedabove may include any of the hardware and/or software described above.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising”, “having”, “including”, and “containing”are to be construed as open-ended terms (i.e. meaning “including, butnot limited to”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein.

The use of any and all examples, or exemplary language (e.g. “such as”)provided herein, is intended merely to better illuminate the disclosureand does not pose a limitation on the scope of the disclosure unlessotherwise claimed.

The present disclosure has been provided above by way of example only,and it will be appreciated that modifications of detail can be madewithin the scope of the claims which define aspects of the invention.

1. An apparatus for linking transmedia content subsets comprising: amemory configured to store: a plurality of transmedia content data itemsand associated linking data which define time-ordered content linksbetween the plurality of transmedia content data items, the plurality oftransmedia content data items being arranged into linked transmediacontent subsets comprising different groups of the transmedia contentdata items and different content links therebetween; a transmediacontent model that represents the transmedia content data items as nodesand the content links between the transmedia content data items as edgesin one or more time-varying graphs; processing circuitry configured to:associate the transmedia content data items with the time-orderedcontent links and store the linking data in the memory; assign thetransmedia content data items to nodes of a graph structure, assign thetime-ordered content links to edges of the graph structure; and storedata pertaining to the nodes and edges in the transmedia content model.2. The apparatus of claim 1, wherein the processing circuitry is furtherconfigured to implement a harvesting engine configured to: identify anumber of common nodes and/or a number of common edges shared between atleast two of the linked transmedia content subsets; and extract from thememory one or more linked transmedia content subsets that share commonedges and/or nodes when the number of common edges and/or nodes exceedsa pre-defined threshold.
 3. The apparatus of claim 2, wherein theharvesting engine is configured to extract the one or more linkedtransmedia content subsets by copying the transmedia content data itemsand associated linking data of the extracted transmedia content subsetto another region in the memory.
 4. The apparatus of claim 2, whereinthe harvesting engine is configured to extract the one or more linkedtransmedia content subsets by storing a reference to each transmediacontent data items and each item of associated linking data of theextracted transmedia content subset in the memory.
 5. The apparatus ofclaim 1, wherein the transmedia content data items relate to narrativeelements of the transmedia content data items.
 6. The apparatus of claim5 wherein the time-ordered content links define a narrative order of thetransmedia content data items.
 7. The apparatus of claim 1, wherein eachtime-ordered content link defines a directional link from a firsttransmedia content data item to a second transmedia content data item ofthe plurality of transmedia content data items.
 8. The apparatus ofclaim 7, wherein the first transmedia content data item has a pluralityof outgoing time-ordered content links.
 9. The apparatus of claim 7,wherein the second transmedia content data item has a plurality ofincoming time-ordered content links.
 10. The apparatus of claim 7,wherein the memory is further configured to store a plurality of subsetentry points for the plurality of transmedia content subsets.
 11. Theapparatus of claim 10, wherein each subset entry point is a flagindicating a transmedia content data item that has at least one outgoingtime-ordered link and no incoming time-ordered links.
 12. The apparatusof claim 11, wherein each linked transmedia content subset defines alinear path, wherein a linear path comprises a subset entry point, oneor more transmedia content data items and one or more time ordered linksbetween the subset entry point and the transmedia content data items.13. The apparatus of claim 12, wherein two or more transmedia contentsubsets share one or more subset entry points, one or more transmediacontent data items and/or one or more time ordered content links. 14.The apparatus of claim 13, wherein the two or more transmedia contentsubsets form a non-linear network of transmedia content data itemsconnected by time ordered content links, and wherein the non-linearnetwork includes a plurality of subset entry points and/or a pluralityof subset end points.
 15. The apparatus of claim 14, wherein theprocessing circuitry is further configured to calculate an entropy valuefor a transmedia content subset based on the number of transmediacontent data items and/or time-ordered content links shared with othertransmedia content subsets in the same non-linear network.
 16. Theapparatus of claim 14, wherein the processing circuitry is furtherconfigured to calculate a temperature value for a transmedia contentsubset based on a number of social media endorsements received by thetransmedia content subset or transmedia content data items present inthe transmedia content subset.
 17. The apparatus of claim 16, whereinthe processing circuitry is further configured to calculate a volumevalue for the transmedia content subset based on the number oftransmedia content data items present in the transmedia content subset.18. The apparatus of claim 17, wherein the processing circuitry isfurther configured to calculate a pressure value based on the calculatedtemperature and volume values.
 19. The apparatus of claim 18, whereinthe processing circuitry is further configured to extract the one ormore linked transmedia content subsets for which one or more of theentropy, pressure, volume or time exceeds a predetermined threshold. 20.The apparatus of claim 1, wherein the memory is further configured tocomprise a plurality of transmedia content metadata items, eachtransmedia content metadata item being associated with one or moretransmedia content data items, and a plurality of subset metadata items,each subset metadata item being associated with one or more transmediacontent subsets.
 21. The apparatus of claim 20, wherein each transmediacontent metadata item defines narrative information for each transmediacontent data item.
 22. The apparatus of claim 21, wherein the step ofextracting is carried out only for transmedia content subsets in whichthe transmedia content metadata items indicate a complete narrative. 23.An apparatus for generating metadata for non-linearly connectedtransmedia content comprising: a memory configured to store: a pluralityof transmedia content data items and associated linking data whichdefine time-ordered content links between the plurality of transmediacontent data items, whereby the plurality of transmedia content dataitems are arranged into linked transmedia content subsets comprisingdifferent groups of the transmedia content data items and differentcontent links therebetween; and one or more metadata items associatedwith one or more of the plurality of transmedia content data items; ametadata generation engine configured to generate metadata associatedwith a given subset of transmedia content data items based on one ormore of the metadata items associated with the transmedia content dataitems comprised within the given subset.
 24. The apparatus of claim 23,wherein the metadata generation engine is configured to generatemetadata associated with the subset of transmedia content data items bygrouping the metadata associated with the transmedia content data itemsof the transmedia content subset and copying the grouped metadata toanother region in the memory.
 25. The apparatus of claim 23, wherein themetadata generation engine is configured to generate metadata associatedwith the subset of transmedia content data items by grouping themetadata associated with the transmedia content data items of thetransmedia content subset and copying references to the grouped metadatato another region in the memory.
 26. The apparatus of claim 23, whereinthe metadata generation engine is further configured to generate the oneor more metadata items associated with the plurality of transmediacontent data items stored in the memory.
 27. The apparatus of claim 26,wherein the metadata generation engine is configured to generateautomatically metadata by analysing media elements or graph structures.28. The apparatus of claim 27, wherein the metadata generation engine isconfigured to extract and analyze features from the transmedia contentdata items.
 29. The apparatus of claim 23, wherein the metadatageneration engine is configured to generate metadata from input receivedat a user input interface.
 30. The apparatus of claim 30, wherein themetadata generation engine further comprises a metadata aggregator,wherein the metadata aggregator is configured receive metadata from oneor more sources, analyse the received metadata and associate theanalysed metadata with the transmedia content data items and transmediacontent subsets.
 31. The apparatus of claim 30, wherein the metadataaggregator analyses the metadata received from the plurality of sourcesto determine metadata tags to associate with the transmedia content dataitems and transmedia content subsets.
 32. A system comprising: theapparatus of claim 1; and an electronic device configured to be incommunication with the apparatus and display one or more of thetransmedia content items received from the apparatus.
 33. A method forlinking transmedia content subsets formed of a plurality of transmediacontent data items and associated linking data which define time-orderedcontent links between the plurality of transmedia content data items,the transmedia content subsets comprising different groups of thetransmedia content data items and different content links therebetween,the method comprising: associating the transmedia content data itemswith the time-ordered content links and storing the linking data in thememory; and assigning the transmedia content data items to nodes of agraph structure; assigning the time-ordered content links to edges ofthe graph structure; and storing data pertaining to the nodes and edgesin a transmedia content model.
 34. A method for generating metadata fornon-linearly connected transmedia content comprising a plurality oftransmedia content data items and associated linking data which definetime-ordered content links between the plurality of transmedia contentdata items, the plurality of transmedia content data items beingarranged into linked transmedia content subsets comprising differentgroups of the transmedia content data items and different content linkstherebetween, the method comprising: generating, with a metadatageneration engine, metadata associated with a given subset of transmediacontent data items based on one or more metadata items associated withone or more of the plurality of transmedia content data items within thegiven subset.
 35. A computer readable medium comprising computerexecutable instructions, which when executed by a computer, cause thecomputer to perform the steps of the method of claim
 33. 36. A computerreadable medium comprising computer executable instructions, which whenexecuted by a computer, cause the computer to perform the steps of themethod of method of claim 34.